Histopathological Examination of Intervertebral Disc Specimens: A Cost-Benefit Analysis
Bibliographic record
Abstract
OBJECT: Routine histopathological examination of intervertebral disc specimens is commonly performed in North American hospitals, but recent studies have questioned the utility of this practice in cases where the indication for surgery is a benign process such as degenerative disc disease. In this study, we have performed a cost-benefit analysis of this practice. METHODS: We performed a cost-benefit analysis of routine histopathological examination of 1775 routine (non-neoplastic and non-infectious indications for surgery) and 70 non-routine (suspected neoplastic or infectious indications for surgery) discectomy specimens obtained over an eight-year period (1996 and 2004). Chart reviews were used to determine if any histopathology findings were clinically significant (i.e., affected subsequent patient care). Total costs were calculated. A literature review was conducted to compare our results with other published series. RESULTS: We found four unexpected histopathology results among 1775 specimens obtained from routine cases, one of which was clinically significant. We calculated costs of $42,165.25 per unexpected histopathological finding and $168,625 per clinically significant histopathological finding. For non-routine surgeries, the cost per abnormal pathological finding was $116.67. CONCLUSIONS: In routine cases, histopathological examination of disc specimens is not justified. The decision to send specimens for pathological examination should be based on the surgeon's judgment.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".