Reversible parkinsonism and MRI diffusion abnormalities in cortical venous thrombosis
Bibliographic record
Abstract
Introduction The number of lymph nodes evaluated in resected colorectal adenocarcinoma specimens is associated with patient survival and therefore used as a marker of quality in colon cancer care.1 The aim of this study was to assess whether individual surgeons and pathologists retrieving and analysing colorectal resection specimens at our centre differed in their lymph node yields (LNY). Methods Casenotes for all elective Right Hemicolectomies (RHC) and Anterior Resections (AR) performed at our centre for adenocarcinoma between 2008–2009 were identified using hospital coding and histology reports were assessed for LNYs. Total and positive LNYs were compared between surgeons and pathologists using GnuPSPP statistical software. Results Two dedicated colorectal surgeons and 5 pathologists produced 77 RHC and 125 AR (of which 62 were Total Mesorectal Excisions (TME)) specimens. Median total LNYs for each of our surgeons were: 22(16.5–28.6) and 23(17–31) p = 0.95 for RHC; 21(12–28) and 16(12.5–27) p = 0.37 for all ARs; and 19(9.5–25) and 15(12–24) p = 0.13 for TMEs. Positive LNYs from RHC (p = 0.5), AR (p = 0.80) and TME (p = 0.23) were also similar between the surgeons. Of the 5 pathologists the highest and lowest median total LNYs for RHC, AR and TME specimens were: 28(21–32) to 16.5(14.5–18.5); 28(21–35) to 14.5(10–19.72); and 25(16.5–34) to 9(8.5–16.5), respectively. Oneway ANOVA testing of the 5 pathologists total LNYs for RHC, AR and TME gave p values of 0.02, 0.00 and 0.01, respectively. The pathologist with the lowest LNY after TME had significantly more specimens (57%) treated with neoadjuvant radiotherapy than the other pathologists. Comparison of positive LNYs between pathologists showed no differences in yields with p = 0.05 for RHC, p = 0.80 for AR and p = 0.23 for TME. Conclusion Lymph node yields following colorectal resection at our centre are far higher than published yields from other centres.2 No difference was seen in the LNYs delivered by the 2 surgeons studied; however, there was a significant difference in total LNYs between our 5 pathologists; with some pathologists consistently producing huge yields. This did not, however, translate to a difference in positive LNYs produced by our pathologists although our study is underpowered to demonstrate equivalence.
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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 | 0.000 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".