***TASK*** the task is to summarize an input biomedical literature in six sentences 
    ***INPUT*** the input is a biomedical literature 
    ***OUTPUT*** the output is the summary of an input biomedical literature in six sentences 
    ***DOCUMENTATION*** 
    ***EXAMPLES*** 
    Input: Context Quality -of-care evaluation often focuses on how often patients receive certain tests or treatments . Theoretically , the content of care should predict patient survival , but the evidence is inconclusive . Contribution This study used 207 criteria to assess good care in 372 vulnerable elderly patients . When care did not meet these st and ards , patients were more likely to die during the 3 years of follow-up . Implication s In vulnerable older patients , the content of care is associated with mortality . This finding supports the use of process measures in the evaluation of quality of care and shows that good care may prolong life . The Editors As clinicians , the public , and health systems become more aware that many Americans do not receive necessary care , the importance of measuring and improving quality of care has gained increasing attention ( 1 - 3 ) . Although quality of care can theoretically be measured by outcomes ( what happens to patients ) , process ( what providers do ) is often preferred ( 3 - 5 ) because process is under relatively greater control of providers , needs a shorter time frame , can directly inform improvement , and may not require statistical adjustment for severity of illness ( 6 , 7 ) . Typically , process measures evaluate the proportion of eligible patients who receive care as recommended ( for example , the proportion of patients 65 years of age receiving pneumococcal vaccine ) . To be a meaningful measure of quality , a process of care must be related to improved patient outcomes . For many quality indicators , this relationship is based on evidence of efficacy from r and omized , controlled trials , usually among a select patient population . However , the relationship between performance on  process of care quality indicators  and better health outcomes remains a largely untested assumption for general population s of patients receiving care in community setting s. The lack of a demonstrated relationship between performance on process quality measures and outcome advantage in a cohort of patients has hindered the acceptance of quality indicators as a way to measure and improve health outcomes ( 8) . The Assessing Care of Vulnerable Elders ( ACOVE ) project developed a set of process quality criteria  that were judged by clinical experts to improve patient outcomes on the basis of clinical evidence and professional opinion ( 9 - 11 ) . Combined with mortality information available through the National Death Index , our study evaluated the process outcome relationship . While the development method conferred content validity on the process measures , we aim ed to assess the predictive validity of the quality measurement system by examining the relationship between the quality of care received by sample d participants and their subsequent survival . Methods The ACOVE Project The ACOVE project developed and implemented a set of quality indicators that focuses on process of care for clinical conditions important in the care of vulnerable older patients . Details of the methods of selecting conditions and developing quality indicators have been described in previous reports ( 9 , 10 ) 12 ) . We selected quality indicators by using systematic review s of the medical literature followed by deliberations by several panels of clinical experts using formal consensus methods to assess the validity of quality indicators . This process result ed in 236 quality indicators covering 22 clinical areas ( continuity of care , dementia , depression , diabetes mellitus , end-of-life care , falls and mobility problems , hearing loss , heart failure , hospital care , hypertension , ischemic heart disease , malnutrition , medication management , osteoarthritis , osteoporosis , pain management , pneumonia , pressure ulcer , screening and prevention , stroke and atrial fibrillation , urinary incontinence , and vision care ) across the continuum of care , including prevention , diagnosis , treatment , and follow-up . Each quality indicator contains an if clause that defines the patient who is eligible to receive it and a then clause that describes what care is recommended ( for example : If a vulnerable elder has had a myocardial infa rct ion , then he or she should be offered a -blocker ) . If the medical record describes a contraindication to the recommended care , the patient is not eligible for the quality indicator . Furthermore , we explicitly defined certain indicators as being not applicable , and therefore not included , when assessing the care of patients with advanced dementia or poor prognosis ( 13 ) . We applied the ACOVE quality indicators to a sample of vulnerable older patients in 2 large managed care organizations , 1 in the northeastern United States and the other in the southwestern United States ( 11 ) . Each managed care plan had more than 20000 senior members and contracted with a network of providers for delivery of care . Eligibility criteria included continuous enrollment in the managed care organization with no out-of-network care during the 13-month study period and no active treatment for malignant conditions except for nonmelanoma skin cancer . We identified vulnerable older persons by telephone interview using the Vulnerable Elders Survey-13 ( VES-13 ) ( 14 ) . The VES-13 is a 13-item question naire that produces a vulnerability score ranging from 0 to 10 based on age , self-reported health , and function . Patients with scores of 3 or higher are at 4 times the risk for death or functional decline over the next 2 years and are therefore defined as vulnerable . We excluded nonEnglish- language speakers because interviews were available only in English . Among 3207 community-dwelling patients 65 years of age and older who were r and omly selected from the 2 managed care plans , we conducted screening interviews with 2278 patients ( 9 % through proxies ) and identified 475 ( 21 % ) patients as vulnerable . Among them , 420 ( 88 % ) patients consented to participate in the study and 372 ( 78 % ) patients had medical records for the 13-month period from 1 July 1998 to 31 July 1999 that were able to be abstract ed . We collected all participants ' medical records , including those for inpatient care , outpatient care , nursing home care , home care , and mental health care . Trained nurses abstract ed charts to apply quality indicators . A senior nurse review er assessed completed abstract ions , and physician overreaders review ed them for clinical assessment . We evaluated inter-rater reliability by re abstract ion of 10 % of the medical records , which contained 698 quality indicators . Agreement was 97 % for quality indicator eligibility and 95 % for overall quality score . We collected patient characteristics , including age , sex , cognitive function measured by the Blessed OrientationMemoryConcentration test ( 15 ) , and mental health score derived from Medical Outcomes Study Short Form-36 items ( 16 ) , at the time of the recruitment telephone interview . The R AND institutional review board approved the study protocol . Among the 236 ACOVE quality indicators , 207 could be implemented in the field trial either by medical record  ( 183 indicators ) or patient interview  ( 24 indicators ) . Because some patients died before the interview was conducted , we used only quality indicators for which information was available in medical records . Among these , 160 quality indicators had at least 1 eligible patient ; 43 focused on prevention , 42 on diagnosis , 47 on treatment , and 28 on follow-up care . These 160 quality indicators covered all 22 conditions . The Appendix Table contains the list of quality indicators used in our report , the number of eligible patients , and the pass rate for each indicator . Appendix Table . Quality Indicators Using Medical Records as the Information Source , Eligible Patients , and Pass Rates Statistical Analysis We calculated quality scores for each patient on the basis of the percentage of ACOVE quality indicators for which an eligible patient received recommended care . We obtained death , date , and cause-of-death data for ACOVE participants from the National Death Index during 3 years after the quality measurement period ( from August 1999 to September 2002 ) . We used both unadjusted and adjusted analyses to examine the link between patient survival and quality score . For the unadjusted analysis , we first divided the sample in half on the basis of quality score ( that is , median and < median ) and examined the difference in survival curves between patients with higher quality and patients with lower quality by using the log-rank test . Second , we calculated survival for 10 equal intervals of quality score from the lowest quality score to the highest quality score in the sample and graphically assessed the grade d relationship between quality score and survival . We used the Cox proportional hazards survival model in adjusted analyses . Because the proportional hazards assumption for the multivariate survival analysis did not hold for the entire observation period , we used a piecewise model that allowed the coefficients for quality to vary between 500 days or less and more than 500 days , as suggested by the KaplanMeier survival curve in the unadjusted analysis . Covariates included sex , VES-13 score ( including age ) , mental health , number of hospitalizations and office visits during the quality measurement period , and number of conditions that patients had during the quality measurement period among 13 comorbid conditions ( dementia , depression , diabetes mellitus , heart failure , hypertension , ischemic heart disease , osteoarthritis , osteoporosis , pressure ulcer , atrial fibrillation , urinary incontinence , chronic obstructive pulmonary disease , and chronic renal failure ) . The mental health score ranged from 1 to 6 , and we created 3 categories on the basis of the score ( < 2 , very good ; 2 to 3 , good ; and > 3 , fair ) . An indicator variable design ated patients who were not interviewed for mental health items because of cognitive impairment . To further examine a plausible mechanism for the quality survival link , we examined the relationship between survival and OBJECTIVE To evaluate the pain experience of very old hospitalized patients during and up to 1 year after hospitalization . To underst and the relationship of level of pain to demographic , psychological , and illness-related variables . DESIGN Prospect i ve cohort study . SETTING Four teaching hospitals . PARTICIPANTS 1266 patients at least 80 years of age in the Hospitalized Elderly Longitudinal Project ( HELP ) . MEASUREMENTS  Pain interviews during hospitalization and 2 and 6 months later . Ordinal logistic regression was used to study the association of variables with level of pain . RESULTS Interviews about symptoms were available for 806 ( 64.6 % of survivors ) patients during hospitalization , 614 ( 57.9 % of survivors ) at 2-months , and 416 ( 48.0 % of survivors ) at 12 months ; of these , 45.8 , 49.8 and 53.6 % , respectively , reported pain , and 12.9 % of those with pain during hospitalization were dissatisfied with its control . Multivariable analysis revealed that study hospital , admission diagnosis , depressed mood , alertness , and level of activity 2 weeks before admission were associated with pain during hospitalization , and pain reported during hospitalization , study site , patient level of activity 2 weeks before hospital admission , and patient education were associated with pain 2 months later . CONCLUSIONS Frequency of pain among very old hospitalized patients and at follow-up is similar to that reported for other hospitalized patients . Further studies of strategies to better control pain during and after hospitalization in very old patients are needed . These studies will have to adjust for other variables associated with pain in the oldest-old OBJECTIVE Long-term pain is a common sequela of thoracotomy , occurring in approximately 50 % of patients 2 years after thoracic surgery . Despite this alarming statistic , little is known about the factors responsible for the transition of acute to chronic pain . The aim of the present study is to identify predictors of long-term post-thoracotomy pain . DESIGN Follow-up was for 1.5 years for patients who had participated in a prospect i ve , r and omized , controlled trial of preemptive , multimodal analgesia .  SETTING Subjects were recruited from a tertiary care center . PATIENTS Thirty patients who had undergone lateral thoracotomy  were followed up by telephone , administered a structured interview , and classified according to long-term pain status . MAIN OUTCOME MEASURES Present pain status was measured by a verbal rating scale ( VAS ) . Measures obtained within the first 48 h after surgery were compared between patients with and without pain 1.5 years later . These include VAS pain scores at rest and after movement , McGill Pain Question naire data , patient-controlled morphine consumption ( mg ) , and pain thresholds to pressure applied to a rib contralateral to the thoracotomy incision . RESULTS Fifty-two percent of patients reported long-term pain . Early postoperative pain was the only factor that significantly predicted long-term pain . Pain intensity 24 h after surgery , at rest , and after movement was significantly greater among patients who developed long-term pain compared with pain-free patients . A significant predictive relationship was also found at 24 and 48 h using the McGill Pain Question naire . Cumulative morphine was comparable for the two groups . Pain thresholds to pressure applied to a rib contralateral to the incision did not differ significantly between the groups . CONCLUSION Aggressive management of early postoperative pain may reduce the likelihood of long-term post-thoracotomy pain OBJECTIVE To compare the experience of pain and treatment of pain  in cognitively impaired and cognitively intact older adults after surgical repair of a hip fracture . DESIGN Prospect i ve comparative survey design . PARTICIPANTS A convenience sample of 88 hip fracture patients ( 53 cognitively impaired , 35 cognitively intact ) from three Midwestern urban hospital orthopedic units was interviewed between days 2 and 5 postoperatively . Subjects whose Folstein Mini-Mental State Exam ( MMSE ) score was less than or equal to 23 were categorized as impaired . RESULTS Pain report and intensity did not differ significantly between the two groups . One-third of the subjects in both groups rated pain as severe or worse . Cognitively impaired subjects scored significantly higher on the Checklist of Nonverbal Pain Indicators observed with movement ( CNPI-m ) than did cognitively intact subjects . Cognitively impaired subjects received significantly less  opioid analgesics  than cognitively intact subjects in the first and second 48 hours postoperatively . Both groups received less than 2 
    Output:
    
These quality indicators will help research ers and clinicians target quality improvement efforts .