Bibliographic record
Abstract
ge wh lik ac sim mo saf rat for wi su ha rat an in av gro Th ca the dif pli ca are rec cu he rep co wo ge we ce for A U. dropped nuclei or an intraocular lens power miscalculation, practices that would not be captured in the study methods (w su pe ca tha wh the be tha Ca rat on ha pa On po vo ch ex ex pa su cia ca dif tha no tio res ou da ev the ha no oc dis mi mo by for co res wo me results of the analysis. Similarly, could higher volume surgeons operate on dif-© 2 Pubhich use only separate claims from 1 to 14 days after rgery). On the other hand, administrative claims data on a rson/patient level have the advantage of capturing all the re that a patient receives, even if from a provider other n the one who performed the surgery. ferent types of cataracts than lower volume surgeons? It is unclear from the literature whether this would make any difference,8,9 but anectodotal advice suggests that earlier cataracts may be easier to remove. Because there are wait-ing lists in Canada, one might expect that there would not be
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
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 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.039 | 0.030 |
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".