Shunt insertion in the summer: is it safe?
Bibliographic record
Abstract
OBJECT: The potential for increased complications related to the arrival of new residents in July each year has not previously been demonstrated in the neurosurgical literature. The authors investigated this phenomenon in children undergoing cerebrospinal fluid shunt surgery. METHODS: Data were obtained from a multicenter hydrocephalus clinical trials database and from hospital admission records in English-speaking Canada. Data pertaining to patients treated in July and August were compared with those pertaining to patients treated during the remainder of the year. The incidence of shunt failure, shunt infection, neurological deficits, wound infection, technical errors, and death were compared using a chi-square test for categorical outcomes, means for continuous outcomes, and survival analysis for time-dependent outcomes. In the hydrocephalus clinical trials database, 138 of 737 patients were treated in July and August. The median duration of shunt lifespan (hereafter referred to as "shunt survival") was 1.7 years for patients treated during the summer months and 2.4 years for those treated throughout the rest of the year (p = 0.10); for shunt infection the figures were 13.8 and 8.8% (p = 0.08) of the total number of cases, and for wound dehiscence they were 2.9 and 0.7% (p = 0.05), respectively. When all shunt procedures were included, an examination of shunt survival and infection incidence rates recorded in the Canadian Hospital Discharge Database seemed to imply a significant advantage to having surgery between September and June (log-rank statistic = 7.10, p = 0.008). CONCLUSIONS: The data suggest a "July effect" on some outcomes related to shunt surgery, but the effect was small. Nonetheless, the potential morbidity of shunt failure, infection, and the cost of treatment indicate that continued vigilance and appropriate supervision of new staff by attending surgeons is warranted.
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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 | 0.004 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".