Incidence of unusual and clinically significant histopathological findings in routine discectomy
Bibliographic record
Abstract
OBJECT: Routine histopathological examination of discectomy specimens remains common practice in many hospitals, although it rarely detects unsuspected clinically significant disease. Controversy exists as to the effectiveness of this practice. The objectives of this study were to compare the authors' experience with a review of the literature. METHODS: In a retrospective database analysis the authors identified all intervertebral disc specimens obtained during spinal procedures over an 8-year period (1996-2004). Cases of benign (nonneoplastic and noninfectious) indications for surgery were included in the study, whereas cases of nonbenign indications were excluded. The final pathological diagnoses were reviewed, and a chart review was performed to determine whether any unexpected findings affected subsequent patient care. A total of 1858 discectomy specimens were identified: 1775 of these were obtained in 1719 routine discectomy procedures. Unexpected histopathological findings were identified in four cases, and none was clinically significant. CONCLUSIONS: Routine histopathological examination of disc specimens is not justified. The decision to send specimens for pathological examination should be determined on a case-by-case basis after consideration of the clinical presentation, results of laboratory and imaging studies, and intraoperative findings.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".