© 2002 Canadian Medical Association or its licensors
Bibliographic record
Abstract
Complaints: (1) A reviewer calls to say he will never review another paper for CMAJ because we accepted a paper he had urged us to reject in view of the authors ’ undisclosed and close re-lationship with a pharmaceutical company. (2) A letter writer complains that we edited her let-ter so severely that the main point was utterly lost. (3) An author complains that his paper was rejected even though it was “obvious [we] had not even read it. ” Another complains that we misplaced his paper for 8 months, only to reject it when we found it. Conundrums: (1) In their report on a trial of chemotherapy regimens in patients at a community hospital, the authors do not mention obtaining either patient consent or approval from a research ethics board. (2) Another paper, this time from a major university, describes a small study involv-ing outpatients; the participants gave informed consent, but the authors neglected to obtain ap-proval from a research ethics board. (3) An author who was asked during final editing of her pa-per to provide additional data simply fabricated the numbers; luckily, this was detected before the paper appeared in print. (4) An author calls to say that his institution has refused to allow him to submit a report on a series of deaths that occurred at his institution and which he believes might have been caused by the incorrect prescribing of a commonly used drug. Complaints and conundrums: an ombudsman–ethicist for CMAJ
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Insufficient payload (model declined to judge) Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
| gpt | Insufficient payload (model declined to judge) Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.011 | 0.005 |
| Insufficient payload (model declined to judge) | 0.826 | 0.790 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".