Of 3700 children thought to have non-cardiac chest pain at initial paediatric cardiology clinic evaluation, none suffered cardiac death over a median of 4 years follow-up
Bibliographic record
Abstract
Commentary on: Saleeb SF, Li WY, Warren SZ, et al. Effectiveness of screening for life-threatening chest pain in children. Pediatrics 2011;128:e1062–8.[OpenUrl][1][Abstract/FREE Full Text][2] Chest pain (CP) in children is a common complaint, though cardiac causes are rare. The study by Saleeb et al aims to determine whether any children diagnosed initially with CP in the cardiology clinic of a children's hospital subsequently died as a result of a cardiac condition. Medical records were reviewed for all clinic patients >6 years of age with an International Classification of Diseases-9 (ICD-9) code for initial CP assessment who were seen at Children's Hospital Boston over 10 years. The authors suggest that usual coding practice in the clinic would be to use such codes for patients not considered to have cardiac disease. Demographic features, history and exam findings, tests performed and presumed diagnoses were abstracted. The vital status of all patients, and cause of death of deceased patients, were … [1]: {openurl}?query=rft.jtitle%253DPediatrics%26rft_id%253Dinfo%253Adoi%252F10.1542%252Fpeds.2011-0408%26rft_id%253Dinfo%253Apmid%252F21987702%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [2]: /lookup/ijlink?linkType=ABST&journalCode=pediatrics&resid=128/5/e1062&atom=%2Febmed%2F17%2F6%2F190.atom
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: Editorial About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| gpt | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Observational | medium |
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".