Impeded Delivery of Pathology and Laboratory Medicine Services by Corruption Is Not Unique to Resource-Limited Settings
Bibliographic record
Abstract
To the Editor We read with interest the article by Glynn et al,1 which provides a systematic review of corruption practices in pathology and laboratory medicine (PALM) and how such practices (1) contribute to undermining health care goals, (2) increase disparities in care, and (3) result in poor health care outcomes, especially for the most vulnerable in society. The authors perform this review with respect to the health sectors of low- and middle-income countries (LMICs). One interesting distinction drawn is that corruption occurring in LMICs is often felt to be “need based,” whereas in high-income countries (HICs; eg, United States), it is considered more “greed based.” However, we would assert that the institutionalized vs individual characterization of corruption more accurately differentiates between the two.2 Individual corruption is the intentional abuse of “office” for personal, financial gain. It is considered fraudulent and illegal in all countries. It is generally...
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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 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.020 | 0.023 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".