A Consult Is Just a Page Away: A Prospective Observational Study on the Impact of Jinxing on Call Karma in Neurosurgery
Bibliographic record
Abstract
OBJECTIVE: Our aim was to assess the impact of jinxing on "call karma" in neurosurgery. METHODS: We conducted a prospective observational study on 15 residents on call for the neurosurgery service, recording the total number of admissions, consults, deaths encountered, surgeries performed, hours of sleep and subjective call rating on a numeric rating scale (NRS) of 0-10 in terms of general awfulness. RESULTS: Some 204 on-call nightshifts were analyzed, of which 61 (29.9%) were jinxed and 143 (70.1%) were nonjinxed. Jinxes seemed to occur in clusters. The baseline parameters (experience, type of call coverage and superstition level) of the study groups were well balanced. A trend toward more surgeries was observed during jinxed nights, where residents slept significantly less (mean 147.8±96.2 vs. 180.9±106.1 min, p=0.037) and rated their on-call experience worse on the NRS (4.4±2.2 vs. 3.5±2.0, p=0.011), while there was no significant difference in number of admissions, consults or deaths. CONCLUSIONS: The act of jinxing ought to be avoided in the neurosurgical setting, as it might be potentially harmful to resident call karma, irrespective of level of experience, resources and personal beliefs.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".