Maintenance of germ line and somatic DNA methylation during mouse development
Bibliographic record
Abstract
We used the Health Services Commission data from Manitoba, Canada to identify complications resulting from hysterectomy, cholecystectomy, and prostatectomy which led to hospital readmissions. For each procedure, two specialists independently judged whether the readmissions were for surgery-related complications on the basis of liberally interpreted literature guidelines. Then, each pair of physicians met to resolve differences; only complications agreed upon by physicians were retained in our computer-based analysis. The analysis was done in three steps: algorithms were developed using guidelines from the literature, physician input, and 1974 hospital claims; these were then modified using 1975 data; finally, the algorithms were tested with 1976 data. The computerized algorithms developed were compared with the clinical decisions of physician panels. The results showed high specificity, sensitivity, and predictive value. Given the increasing availability of routinely collected data bases, the possibilities for inexpensively monitoring the outcomes of different providers and institutions are appealing. More extensive validation and application of the methodology to a greater number of procedures are necessary to implement such a program.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".