Can surgeon familiarization with current evidence lead to a change in practice? A prospective study
Bibliographic record
Abstract
BACKGROUND: Despite evidence against its utility, many surgeons continue to employ prophylactic nasogastric decompression in elective colonic resection. This study aimed to establish whether an easy and practical intervention, mailing out a summary of current evidence to surgeons, can change surgeons practice to bring it more in line with current evidence. METHODS: The use of prophylactic nasogastric (NG) decompression in elective colonic resections was documented for the 2 consecutive months of October and November, 2004 at the Royal Alexandra Hospital (RAH). A one page summary of recent evidence concerning this practice was then mailed to all general surgeons at that institution. A similar second review was carried out for the months of January and February, 2005. The two periods were compared with regards to prophylactic NG use. RESULTS: Twenty two patients underwent elective colonic resections during the months of October and November, 2004. Twenty one patients underwent such procedures in January and February, 2005. Seven out of the 22 cases in the first group (the pre-intervention block) received prophylactic NG decompression. Five out of the 21 cases in the second group (the post-intervention block) received prophylactic NG decompression. The difference in prophylactic NG use between the two groups was not statistically significant. CONCLUSIONS: This study has shown that mailing out a summary of current evidence to surgeons concerning a certain issue is not sufficient to lead to a change in practice.
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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 | 0.099 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".