Can guidelines improve referral to elective surgical specialties for adults? A systematic review
Bibliographic record
Abstract
AIM: To assess effectiveness of guidelines for referral for elective surgical assessment. METHOD: Systematic review with descriptive synthesis. DATA SOURCES: Medline, EMBASE, CINAHL and Cochrane database up to 2008. Hand searches of journals and websites. SELECTION OF STUDIES: Studies evaluated guidelines for referral from primary to secondary care, for elective surgical assessment for adults. OUTCOME MEASURES: Appropriateness of referral (usually measured as guideline compliance) including clinical appropriateness, appropriateness of destination and of pre-referral management (eg, diagnostic investigations), general practitioner knowledge of referral appropriateness, referral rates, health outcomes and costs. RESULTS: 24 eligible studies (5 randomised control trials, 6 cohort, 13 case series) included guidelines from UK, Europe, Canada and the USA for referral for musculoskeletal, urological, ENT, gynaecology, general surgical and ophthalmological conditions. Interventions varied from complex ("one-stop shops") to simple guidelines. Four randomized control trials reported increases in appropriateness of pre-referral care (diagnostic investigations and treatment). No evidence was found for effects on practitioner knowledge. Mixed evidence was reported on rates of referral and costs (rates and costs increased, decreased or stayed the same). Two studies reported on health outcomes finding no change. CONCLUSIONS: Guidelines for elective surgical referral can improve appropriateness of care by improving pre-referral investigation and treatment, but there is no strong evidence in favour of other beneficial effects.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.141 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".