Data for: Development of a Preoperative Questionnaire to Improve Satisfaction with Hallux Valgus Repair: a Delphi Study
Bibliographic record
Abstract
Project Summary Satisfaction with hallux valgus repair is often poor, despite good surgical outcomes. The aim of this study was to develop a new tool to analyse the subjective and objective expectations of individuals during a pre-operative consultation for hallux valgus repair in order to improve post-surgical satisfaction. We first collected the reasons for dissatisfaction with repair from the medical files of dissatisfied individuals. A questionnaire based on the reasons for dissatisfaction was designed. The DELPHI method was used to validate the questionnaire: we submitted the questionnaire to a panel of 34 francophone experts in hallux valgus repair for rating in 4 rounds. Data Abstract We selected francophone experts (from France, Belgium and Canada) by screening the lists of members of relevant academic societies: we verified their curriculum vitae and asked those who had at least 5 years’ experience in the treatment of hallux valgus and who had published or communicated on the subject to participate. The data correspond to the responses of the expert committee to the different rounds of the DELPHI method used for the consensual validation of the questionnaire created. Data were collected via the Drag'n Survey tool (RGPD compatible). The data are organised in the form of a double entry table: each column corresponds to an expert, each row corresponds to the different questions forming the questionnaire submitted to consensus. The data filling the table are thus the scores attributed to each item of the questionnaire by the chosen group of experts. The data are organised in the form of 4 tables, corresponding to the 4 rounds of the DELPHI method. The questionnaires used during the different rounds of the Delphi are also included as separate files.
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 | 0.042 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.039 | 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".