Preoperative assessments by trained nurses were equal in quality to assessments by preregistration house officers
Bibliographic record
Abstract
Kinley H, Czoski-Murray C, George S, et al. Effectiveness of appropriately trained nurses in preoperative assessment: randomised controlled equivalence/non-inferiority trial. BMJ2002 ; 325 : 1323 –6 [OpenUrl][1][Abstract/FREE Full Text][2] QUESTION: Are preoperative assessments by trained nurses equal in quality to those done by preregistration house officers? Randomised (allocation concealed), unblinded, controlled equivalence/non-inferiority trial. 4 hospital sites in 3 UK National Health Service Trusts. 1907 patients who required assessment before general anaesthesia for general, vascular, urological, or breast surgery. 1874 patients (98%) were included in the analysis (mean age 57 y, 49% women). 954 patients were allocated to preoperative assessment by a nurse who had completed master’s level courses in advanced practice or equivalent. 953 patients were allocated to assessment by a preregistration house officer. 1 of 2 specialist registrars in anaesthesia examined each patient … [1]: {openurl}?query=rft.jtitle%253DBMJ%26rft.stitle%253DBMJ%26rft.aulast%253DKinley%26rft.auinit1%253DH.%26rft.volume%253D325%26rft.issue%253D7376%26rft.spage%253D1323%26rft.epage%253D1323%26rft.atitle%253DEffectiveness%2Bof%2Bappropriately%2Btrained%2Bnurses%2Bin%2Bpreoperative%2Bassessment%253A%2Brandomised%2Bcontrolled%2Bequivalence%252Fnon-inferiority%2Btrial%26rft_id%253Dinfo%253Adoi%252F10.1136%252Fbmj.325.7376.1323%26rft_id%253Dinfo%253Apmid%252F12468478%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [2]: /lookup/ijlink?linkType=ABST&journalCode=bmj&resid=325/7376/1323&atom=%2Febnurs%2F6%2F4%2F122.atom
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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 | 0.012 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.001 |
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".