Critical study of pathology theses supported at the medical university of Tunis (2000-2010).
Bibliographic record
Abstract
BACKGROUND: Medical writing is a coded language; its purpose is to convey a scientific message. In pathology, specialty involving the study of cell and tissue, quantitative and qualitative production of medical doctoral theses and their thematic focus has not been studied. The aim of this study was to analyze the pathology theses on the level of form, the background and methodology. METHODS: Descriptive retrospective study of medical doctoral theses in the specialty "Pathology", listed in the catalog of theses of the library of the Faculty of Medicine of Tunis and supported between 2000 and 2010. Each thesis has been subject of a direct reading, systematic and thorough. RESULTS: The study involved 189 pathology theses. The average overall productivity per academic pathologist was 5.5 theses. Gastrointestinal pathology was the most studied theme (24.9%). Tumor pathology was addressed in 74.1% of the theses. The IMRAD structure was respected in 57.7% of theses; by assistant professor than by associate professor and professor (p = 0.005). The summary was structured in 88.3% of theses, comparably with the grade of the thesis director (p = 0.5) and with the grade of PhD student (p = 0.08). The transcript of references did not meet the recommendations of Vancouver in 87.8% of theses and irrespective of the rank of director of thesis (p = 0.2). CONCLUSION: The pathology theses presented some shortcomings, particularly in the quality of medical writing. To remedy this problem, our faculty should increase efforts to improve the quality of scientific work, in order to have a better view of medical research in Tunisia.
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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 | 0.014 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".