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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".