Letter to the Editor, International Journal of COPD [Letter]
Bibliographic record
Abstract
Martin Miller,1 Brendan G Cooper,2 Sanja Stanojevic3 1Institute of Applied Health Sciences, University of Birmingham, Birmingham, UK; 2Lung Function and Sleep, Queen Elizabeth Hospital, Birmingham, UK; 3Community Health and Epidemiology, Dalhousie University, Halifax, NS, CanadaCorrespondence: Brendan G CooperLung Function and Sleep, Queen Elizabeth Hospital Birmingham, Mindelsohn Way, Edgbaston, Birmingham B15 2GW, UKTel +44 121 371 3890Email Brendan.Cooper@uhb.nhs.uk We read the paper by Llordés et al1 with some interest. The results from this small study are interesting but the analysis and conclusion seem to be at odds with the data. The authors consider a COPD diagnosis by both lower limit of normal (LLN) and the fixed ratio (FR), that is FEV1/FVC<0.7, as concordant (LLN+FR+) and subjects who are FR+LLN- as discordant. Their data show that the discordant group have lower CAT score and lower BODE index suggesting that this group likely has other co-morbidities. As expected, the discordant group is older, more maledominated2 and has fewer hospital admissions. Furthermore, the discordant group has a better overall survival and less respiratory mortality which highlights that the discordant group is quite dissimilar to the concordant group. It is not clear how these data clearly demonstrate that using the FR in the diagnosis COPD is superior to LLN. View the original paper by Llordés and colleagues
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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 | 0.002 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.020 | 0.022 |
| Insufficient payload (model declined to judge) | 0.009 | 0.009 |
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".