Routine Pathological Examination of Clinically Presumed Dupuytren Disease: Does It Add Value?
Bibliographic record
Abstract
Background: In surgery for Dupuytren disease (DD), palmar fascia specimens are routinely submitted for pathological evaluation. The purpose of this study was to determine the rate of discordant diagnosis and the value of, and costs associated with, routine pathological analysis of palmar fascia tissue extracted in surgery for clinically diagnosed DD. Methods: All pathology reports for in-house palmar fascia specimens obtained in surgery for clinically diagnosed DD (time period: January 2001 to December 2020) were retrieved from one academic institution. All specimens were classified by a hierarchical free-text string matching algorithm (HFTSMA) and searched for evidence of malignancy. The primary outcome was percentage of concordant, discrepant, and discordant diagnoses. Secondary outcomes included anatomical location and costs. The HFTSMA was used to capture the anatomical location. Costs included professional, laboratory processing, and ancillary fees based on the Ontario Schedule of Benefits. Results: The search retrieved 1323 pathology reports, with 1480 palmar fascia specimens, from 1078 individual patients. By diagnosis, 96.1% of specimens (1422/1480) were concordant (fibromatosis), 3.9% (58/1480) were discrepant (scarring/fibrosis, benign fascia/connective tissue, or other benign findings), and 0% (0/1480) were discordant. The most common location was ring finger (n = 381, 48.7%). Ancillary testing was minimal. The cost per palmar fascia specimen was estimated to be CAD $34.57. The institutional costs were approximately CAD $2558.18/year. Conclusions: Routine pathological examination of specimens in cases of clinically diagnosed DD does not yield additional clinically important findings and may not warrant their costs.
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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 | 0.011 | 0.085 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".