Bibliographic record
Abstract
I appreciate the useful contribution of Dr Preece and his group and I completely agree. The column was intended to focus on the difficult problem of key performance indicators at the management level rather than at clinical level. (This topic is explored more fully in the context of continuous quality improvement in occupational health services in our new book Occupational Health Services: A Practical Approach, 2nd ed, Routledge, 2013.) The National Health Service has been a leader in this effort and hospitals and health care funding agencies in the United States would do well to pay attention. The MoHaWK group is clearly taking a more nuanced approach than merely adapting outcome measures from general medicine and I welcome their insights and recommended measures. Their work links closely with clinical outcomes research but takes the next logical step, by developing measures that are appropriate to occupational outcomes. Identifying key performance indicators for clinical services and outcomes in occupational medicine will permit comparison among groups and facilities, make possible the evaluation of effectiveness (as opposed to efficacy) of clinical guidelines, satisfy managers that they need to use appropriate measures for occupational health services, and allow the study of clinical practice variation for occupational health services. There is another issue, however, which I hesitate to raise but feel I should. Did the tribal governments of the Mohawk (Kanien'gehaga) Nation, indigenous to Ontario and Québec (Canada) and New York, approve the use of their name for the acronym? Here in North America, there is great sensitivity on the part of native peoples to the appropriation of their identities for use in logos, sports teams, mascots, and brand names. Unless approved by tribal authority, the practice is now considered disrespectful, although it remains widespread and usually without intent to offend. Many of us, therefore, now avoid using ethnic or tribal names, even for benign acronyms. Tee L. Guidotti, MD, MPH Medical Advisory Services Washington, DC
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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 | 0.004 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.027 | 0.033 |
| Insufficient payload (model declined to judge) | 0.038 | 0.026 |
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".