A retrospective study ofmortality associated withgeneral anaesthesia inhorses: emergency procedures
Bibliographic record
Abstract
ADAMS,S. M.&McILWRAITH,C. W.(1978) VeterinarySurgery7,63DUCHARME,N. G., HACKETT, R. P., DUCHARME,G. R. &LONG,S. (1983)VeterinarySurgery12, 206EDWARDS,G. B. (1981)Equine VeterinaryJournal13, 158EDWARDS,G. B. (1991)Equine VeterinaryEducation3, 19HUNT, M. J., EDWARDS, G. B. &CLARKE, K. W. (1986) Equine VeterinaryJournal18, 264JOHNSTON,G. M., TAYLOR,P. M., HOLMES,M.A. &WOOD,J. L. N. (1995)Equine VeterinaryJournal27, 193MEE, A. M., CRIPPS, P. J. & JONES, R. S. (1998) Veterinary Record142, 275PARRY, B. W., ANDERSON, G. A. & GAY, C. C. (1983) Equine VeterinaryJournal15, 337PASCOE, P. J., McDONELL, W. N., TRIM, C. M. &VANGORDER,J. (1983)Canadian VeterinaryJournal24, 76REEVES, M.J., CURTIS,C. R., SALMAN, D., REIF,J. S. &STASHAK,T. S.(1990)Preventive VeterinaryMedicine9, 241SVENDSON, C. K., HJANTKJAER, R. K. &HESSELHOLT, M. (1979) NordicVeterinaryMedicine31, 1TEVIK,A. (1983)NordiskVeterinarmedecin 35, 175YOUNG,S. S. &TAYLOR,P. M.(1993)Equine VeterinaryJournal25, 147
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 | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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, 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".