Methodology for Measuring Intraoperative Blood Loss: Protocol for a Scoping Review
Bibliographic record
Abstract
BACKGROUND: At present, there is no standardized method for measuring intraoperative blood loss. Rather, the current data on existing methods is very broad and opaque. In many cases, blood loss during surgery is estimated visually by the surgeon. However, it is known that this type of method is very prone to error. Therefore, better standardized methods are needed. OBJECTIVE: This study aims to conduct a scoping review to present the currently available methods for measuring intraoperative blood loss. This should help to capture the current status and map and summarize the available evidence for measuring blood loss to identify any gaps. METHODS: We will use a state-of-the-art methodological framework. The databases PubMed (MEDLINE) and Cochrane Library will be searched using a search strategy based on the PICO (Population, Intervention, Comparator, and Outcome) scheme. The search period will be limited to January 01, 2012, to December 31, 2023, and our search will be restricted to clinical trials or clinical studies, randomized controlled trials, and observational studies (in line with PubMed definition of study types). Only publications in English and German will be considered. The intention is to identify clinical studies that define "blood loss" as a target criterion or as a primary or secondary end point. EndNote (version 20.6; Clarivate) will be used for the screening process. The data will be collected and analyzed using Microsoft Excel (version 16.77.1). RESULTS: The included studies will be listed in a database, and the following basic data will be extracted: title, year of publication, country, language, study type, surgical specialty, and type of procedure. The number of participants will be listed and the distribution of the participants will be documented in terms of gender and age. The following results are extracted: the type of measurement method used to measure blood loss in this study and whether the parameter "blood loss" was recorded as a primary or secondary outcome. CONCLUSIONS: Currently, there is no comparable review, resulting in ambiguous data regarding the prevailing measurement methods for intraoperative blood loss. The aim of this study is to provide a comprehensive overview-from methods of measurement to various formulae for calculating blood loss-and to establish a status quo. This could then serve as a foundation for further studies. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/58022.
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 | 0.107 | 0.124 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.014 | 0.017 |
| Bibliometrics | 0.024 | 0.021 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.093 | 0.017 |
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".