Bibliographic record
Abstract
> “Oh wad some power the giftie gie us > > To see the world as our patient sees it! > > It wad frae monie a blunder free us, And foolish notion.” > > (With apologies to R Burns) A wrong site procedure is an event in which any medical (frequently surgical) procedure is executed on the wrong body part, or on the correctly named body part but on the wrong side of a patient’s body. If the procedure is surgery then the event is a wrong site surgery or a wrong side surgery. Wrong side surgery is generally considered to be a subset of wrong site surgery. The Joint Commission on the Accreditation of Health Care Organizations (JCAHO) reviewed 15 reported cases of wrong side surgery in a “Sentinel Event Alert” of August 1988. The problem has gained more attention since that time. In 2003, JCAHO promulgated a “Universal protocol for eliminating wrong site, wrong procedure, wrong person surgery”. Our own interest arose (see Acknowledgement) from a chance event. There are no reliable probabilities of various kinds of wrong side and wrong site surgery, because of both poor numerators (number of events) and poor denominators (opportunities for events). JCAHO classifies wrong side surgery as “sentinel events” and accumulates statistics. From 2000 to 2005 there were 3044 sentinel events (categorised in table 1). Wrong site surgery events are near the top of the list and we may assume that about 80% of them were wrong side surgery events (see tables 2 and 3). View this table: Table 1 Classification of 3044 sentinel events reviewed by JCAHO, January 1995 through March 20051 View this table: Table 2 Types of wrong surgery* View this table: Table 3 Surgery error rates in the Veterans Administration system3 As both JCAHO and the Veterans Administration (VA) system show, most wrong site surgery events are wrong side surgeries. The statistics offered are similar. JCAHO reports a wrong side/wrong site surgery ratio of 0.76 (0.59/(0.59+0.19)), and the VA ratio is 0.86 (0.44/(0.44+0.07)). Ericson4 of Washington University in St Louis estimates that …
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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 | 0.026 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".